Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)

• URL: http://arxiv.org/abs/2607.12336v1
• Date: Tue, 14 Jul 2026 04:34:05 GMT
• Title: Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)
• Abstract summary: We propose a CALD-friendly AI-based health misinformation detector and provide a dashboard for medical professionals to analyse this misinformation.<n>We conduct experiments using a Bangla-translated health misinformation dataset to evaluate the performance of various Small Language Models (SLMs)<n>We then design and test a novel health misinformation detection framework grounded in Responsible Natural Language Processing (NLP)<n>This framework incorporates cultural sensitivity, potential for harm, and communication quality, thereby providing a holistic lens for evaluating misinformation in low-resource languages.
• Score: 0.9214837952221063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation. A prevalent challenge in mitigating this issue arises in non-English contexts and low socioeconomic classes, where limited data hinders the training of AI models for effective detection. Consequently, culturally and linguistically diverse (CALD) communities struggle to access trustworthy health information through AI-driven tools. Current AI tools underperform due to a lack of training data and are largely unable to consider language nuances and traditions in non-English contexts. This research addresses these gaps by proposing a CALD-friendly AI-based health misinformation detector and providing a dashboard for medical professionals to analyse this misinformation, a critical step toward mitigating a growing concern among CALD populations. To this end, we conduct a series of experiments using a Bangla-translated health misinformation dataset to evaluate the performance of various Small Language Models (SLMs). SLMs are particularly relevant in this context given the frequent underperformance of Large Language Models (LLMs), which often stems from insufficient domain-specific knowledge and the prohibitive costs of resource-intensive fine-tuning. The results demonstrate that Phi-4 is the superior model, achieving an ideal balance between precision and recall in claim extraction. Then, to mitigate the limitations of SLMs, we design and test a novel health misinformation detection framework grounded in Responsible Natural Language Processing (NLP), which incorporates cultural sensitivity, potential for harm, and communication quality, thereby providing a holistic lens for evaluating misinformation in low-resource languages.

Related papers

• An AI-Based Adaptive Learning Platform for Multilingual and Low-Resource Educational Contexts: A Case Study on Nigeria [2.365610178729227]
  This paper presents an AI-based adaptive learning platform designed for multilingual and low-resource educational contexts.<n>The system integrates fine-tuned large language models (LLMs) within a personalised and adaptive learning framework.<n>To enhance linguistic alignment, a curated Nigerian Pidgin corpus was developed and used to fine-tune an instruction-tuned LLM.
  arXiv  Detail & Related papers  (2026-08-16T13:31:57Z)
• A Patient-Doctor-NLP-System to contest inequality for less privileged [0.688204255655161]
  This study addresses the limited support available to visually impaired users and speakers of low-resource languages such as Hindi.<n>We propose PDFTEMRA, a compact transformer-based architecture that integrates model distillation, frequency-domain modulation, ensemble learning, and randomized activation patterns.<n>Results demonstrate that PDFTEMRA achieves comparable performance with substantially lower computational requirements.
  arXiv  Detail & Related papers  (2025-12-07T08:59:15Z)
• Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models [56.61984030508691]
  We present the first mechanistic interpretability study of language confusion.<n>We show that confusion points (CPs) are central to this phenomenon.<n>We show that editing a small set of critical neurons, identified via comparative analysis with a multilingual-tuned counterpart, substantially mitigates confusion.
  arXiv  Detail & Related papers  (2025-05-22T11:29:17Z)
• Towards Scalable and Cross-Lingual Specialist Language Models for Oncology [4.824906329042275]
  General-purpose large models (LLMs) struggle with challenges such as clinical terminology, context-dependent interpretations, and multi-modal data integration.<n>We develop an oncology-specialized, efficient, and adaptable NLP framework that combines instruction tuning, retrieval-augmented generation (RAG), and graph-based knowledge integration.
  arXiv  Detail & Related papers  (2025-03-11T11:34:57Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey [2.5459710368096586]
  Misinformation transcends linguistic boundaries, posing a challenge for moderation systems.<n>Most approaches to misinformation detection are monolingual, focused on high-resource languages.<n>This survey provides a comprehensive overview of the current research on misinformation detection in low-resource languages.
  arXiv  Detail & Related papers  (2024-10-24T03:02:03Z)
• SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition [55.2480439325792]
  Speech Meta In-Context LEarning (SMILE) is an innovative framework that combines meta-learning with speech in-context learning (SICL)<n>We show that SMILE consistently outperforms baseline methods in training-free few-shot multilingual ASR tasks.
  arXiv  Detail & Related papers  (2024-09-16T16:04:16Z)
• Speaking the Same Language: Leveraging LLMs in Standardizing Clinical Data for AI [0.0]
  This study delves into the adoption of large language models to address specific challenges, specifically, the standardization of healthcare data. Our results illustrate that employing large language models significantly diminishes the necessity for manual data curation. The proposed methodology has the propensity to expedite the integration of AI in healthcare, ameliorate the quality of patient care, whilst minimizing the time and financial resources necessary for the preparation of data for AI.
  arXiv  Detail & Related papers  (2024-08-16T20:51:21Z)
• Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models [1.2281181385434294]
  Large language models (LLMs) offer a valuable technology for various applications in healthcare.<n>Their tendency to hallucinate and the existing reliance on proprietary systems pose challenges in environments concerning critical decision-making.<n>This paper presents a novel HAIC guided deferral system that can simultaneously parse medical reports for disorder classification, and defer uncertain predictions with intelligent guidance to humans.
  arXiv  Detail & Related papers  (2024-06-11T12:41:54Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective [84.78604733927887]
  Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
  arXiv  Detail & Related papers  (2020-10-05T20:49:26Z)
• Data Augmentation for Spoken Language Understanding via Pretrained Language Models [113.56329266325902]
  Training of spoken language understanding (SLU) models often faces the problem of data scarcity. We put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances.
  arXiv  Detail & Related papers  (2020-04-29T04:07:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.